Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China.
Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China; Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
J Allergy Clin Immunol. 2023 Aug;152(2):517-527. doi: 10.1016/j.jaci.2023.03.028. Epub 2023 Apr 7.
Nearly 50 pathogenic genes and hundreds of pathogenic variants have been identified in monogenic autoinflammatory diseases (AIDs). Nonetheless, there are still many genes for which the pathogenic mechanisms are poorly understood, and the pathogenicity of many candidate variants needs to be determined.
Monogenic AIDs are a group of rare genetic diseases characterized by inflammation as the phenotype. With the development of next-generation sequencing, pathogenic genes have been widely reported and used for clinical screening and diagnosis. The International Society for Systemic Autoinflammatory Diseases has recognized approximately 50 pathogenic genes and hundreds of related pathogenic variants in monogenic AIDs. We plan to investigate these pathogenic variants by conducting a variant burden analysis to determine whether or not there are consistent characteristics.
We performed a variant burden analysis on the Genome Aggregation Database cohort using the currently reported genetic variants in monogenic AIDs, analyzing the enrichment of allelic signatures and deleterious predictions at the variants. Allelic signatures were extracted from Genome Aggregation Database, and the deleterious predictions were extracted from existing tools. The features obtained from the variant burden analysis were applied to the Random Forest model to classify the pathogenicity of novel mutations.
Functional enrichment and network analysis of AID pathogenic genes have hinted at the possible involvement of unsuspected signals. The variant burden analysis demonstrated that the pathogenicity of a variant could not be reliably classified using only its allele frequency and deleterious predictions. However, variants of varying classifications of pathogenicity exhibited strikingly different patterns of the allelic signature in the upstream and downstream regions surrounding the variants. Furthermore, the distribution of deleterious variants surrounding the variants in the cohort varied significantly across pathogenicity categories. Finally, the cohort-based features extracted from the alleles were applied to the prediction of pathogenicity in monogenic AIDs, achieving superior prediction performance compared with other tools. The cohort-based features have potential applications across a more extensive variety of disease categories.
The pathogenicity of a variant can be effectively classified on the basis of variant frequency and deleterious prediction of the allele in the cohort, and this information can be used to improve the accuracy of the current classification of the pathogenicity of the variant.
在单基因自身炎症性疾病（AID）中，已经鉴定出近 50 个致病基因和数百个致病变体。尽管如此，仍有许多基因的致病机制尚不清楚，许多候选变体的致病性需要确定。
单基因 AID 是一组以炎症为表型的罕见遗传性疾病。随着下一代测序技术的发展，致病基因已被广泛报道并用于临床筛查和诊断。国际自身炎症性疾病学会已经在单基因 AID 中鉴定出约 50 个致病基因和数百个相关致病变体。我们计划通过进行变异负担分析来研究这些致病变体，以确定是否存在一致的特征。
我们使用单基因 AID 中目前报道的遗传变体在基因组聚集数据库队列中进行了变异负担分析，分析了变体中等位基因特征和有害预测的富集。从基因组聚集数据库中提取等位基因特征，从现有工具中提取有害预测。从变异负担分析中获得的特征应用于随机森林模型，以对新突变的致病性进行分类。
AID 致病基因的功能富集和网络分析暗示可能涉及未被发现的信号。变异负担分析表明，仅使用变体的等位基因频率和有害预测，无法可靠地对变体的致病性进行分类。然而，具有不同致病性分类的变体在变体周围上下游区域的等位基因特征呈现出明显不同的模式。此外，变体周围有害变体的分布在致病性类别之间存在显著差异。最后，从等位基因中提取的基于队列的特征应用于单基因 AID 的致病性预测，与其他工具相比，预测性能更优。基于队列的特征具有在更广泛的疾病类别中应用的潜力。
可以根据变体在队列中的等位基因频率和有害预测有效分类变体的致病性，并且可以利用这些信息来提高当前变体致病性分类的准确性。
